Modeling Parking

With the increasing population in urban areas, finding a vacant parking space has always been the biggest problem for drivers, which causes a series of issues such as energy waste and environmental pollution. Most outdoor parking lots cannot provide details of vacant parking spaces due to limited sensor technology in outdoor environments (i.e., poor resistance to external interference due to heat, light or signal noise). Although some studies are based on images captured by cameras, the existing vacancy detection methods using fixed-position surveillance cameras are not flexible and accurate enough for wide use. We propose an Inclined Bounding Box (IBB) method for detecting vacant parking spaces using aerial images. The IBB method takes advantage of the feature-description ability of deep Convolutional Neural Network (CNN) to identify the status of parking spaces and develops an inclined bounding boxes calibration algorithm on the top layer of CNN architecture. The proposed IBB method can automatically identify parking spaces, locate vacant parking spaces with any direction and detect multiple parking spaces simultaneously. The experimental results show that the proposed IBB method is more accurate than the three counterpart methods.

Finding a vacant parking space in outdoor parking lots is a daily concern of most vehicle drivers during rush hours, especially in the urban context. In this paper, an outdoor parking space vacancy detection system is proposed, using mobile devices to improve parking space searching experience for vehicle drivers by providing them with the location and occupancy information of parking spaces. The system uses state-of-the-art image recognition algorithm, namely Convolutional Neural Network with a Raspberry Pi to identify vacant parking spaces from a parking lot image retrieved in real time via an IP camera. A university parking lot has been chosen as the test bed to deploy the proposed system for real time parking space vacancy detection. An Android smartphone application called Driver App is developed to enable ubiquitous visualization of real time outdoor parking spaces occupancy information for vehicle drivers. Evaluation outcomes based on the responses to System Usability Scale (SUS) questionnaire revealed high usability of the Driver App as a tool that provides smart parking service to assist vehicle drivers in searching for a vacant parking space.

Due to the large volumes of modern vehicles in metropolises, finding a vacant parking space has become an irritating and frustrating problem for drivers, particularly in a congested area, such as sport centers, shopping malls, and downtown [1]. The extra traffic cruising for parking spaces brings serious social problems, including traffic congestion, vehicle accident, fuel waste, and air pollution [2]. Although Google Maps and portable navigators assist drivers to discover parking garages in destinations, drivers are then faced with a problem that no vacant parking space are available [3].

In large parking area attached to shopping malls and so on, it is difficult to find a vacant parking space. In addition, searching for parking space during long time leads to drivers stress and wasteful energy loss. In order to solve these problems, the navigation system in parking area by using ZigBee networks is proposed in this paper. The ZigBee is expected to realize low power consumption wireless system with low cost. Moreover, the ZigBee can form ad-hoc network easily and more than 65000 nodes can connect at the same time. Therefore, it is suitable for usage in the large parking area. In proposed system, the shortest route to the vacant parking space is transmitted to the own vehicle by the ZigBee ad-hoc network. Thus, the efficient guide is provided to the drivers. To show the effectiveness of the proposed parking system, the average time for arrival in the parking area is evaluated, and the performance of the vehicles that equips the ZigBee terminals is compared with the ordinary vehicles that do not equip the ZigBee terminals.

Over the last few years, finding vacant parking spaces has become a hassle for drivers especially in crowded cities. This problem leads to wasting drivers' time, traffic congestion, and air pollution. Recently, smart parking systems aim to address this problem by enabling drivers to have real-time parking information about vacant parking spaces. However, the existing parking systems rely on a central third party to organize the service, which makes them subject to a single point of failure and privacy breach concerns by both internal and external attackers. In this paper, we propose a secure smart parking system using blockchain technology. Specifically, a consortium blockchain is made of parking lots to ensure security, transparency, and availability of the parking system. Then, to protect the drivers' location privacy, we use cloaking technique to hide the drivers' locations. The blockchain validators return available parking offers with in the cloaked area. Finally, the driver selects the best offer and makes reservation directly with the parking lot. Evaluations are conducted to evaluate the proposed scheme, and results indicate practicality of our scheme.

In the large cities, it remains difficult and expensive to create more parking spaces for vehicles since they have almost reached their full occupancy. The lack of available parking places leads to the problem of traffic congestion and consequently to pollution, since drivers will spend a lot of time looking for a vacant parking place. Hence the need to establish a parking management system able to improve the exploitation of the existent parking places at a city level. This paper proposes a new multi agent system for parking vacancies detection based on vision techniques. We explore a network of interconnected video surveillance cameras and parking stations to provide an intelligent service to the drivers in order to facilitate their task and improve the exploitation of the parking resources. The proposed system introduces a new approach for parking spaces modeling and elaborates an adaptive approach for vacancies estimation in order to supply the driver with reliable information about the vacant parking spaces in the city according to the size of his vehicle.

This paper proposes a complete set of Urban Parking Guidance and Information System, which includes Parking Subsystem and City Parking Subsystem. The system adopts wireless sense network based on ZigBee technology for the communication network. The parking information terminals that set up in the parking areas automatically identify the parking vehicles and upload the parking information. After processing the data, the Central Control Module would distribute the parking information by LCD screens for the drivers. In terms of parking supply and demand status, the system sends the separately parking information to the corresponding phone number. The system achieves barrier-free intelligent parking management by realizing functions such as three ways of releasing the information of parking lots, inquiry of parking location and convenient register operation.

A parking guidance and information system for the Shinjuku area of Tokyo is described as Japan's first parking guidance and information system (PGIS) to have a full-fledged guidance function. The concept of the Shinjuku area parking guidance and information system is outlined. The surveys conducted before and after the introduction of the Shinjuku area PGIS confirmed its effectiveness in: (1) improving the convenience of users; (2) reducing on-street parking; (3) enhancing the order of traffic in the Shinjuku area; (4) absorbing into off-street parking lots those cars parked on-street for a short time; and (5) equalizing the number of cars parked between available parking lots. The measures to be taken for further improving the Shinjuku area PGIS are presented at the end.

Real-time information about vacant parking spaces is of paramount value in urban environments. One promising approach to obtaining such information is participatory sensing, i.e. detecting parking/unparking activities using smartphones. This paper introduces and describes multiple indicators, each of which provides an inconclusive clue for a parking or an unparking activity. As a result, the paper proposes a probabilistic fusion method which combines the output from different indicators to make more reliable detections. The proposed fusion method can be applied to inferring other similar high-level human activities that involve multiple indicators which output features asynchronously, and that involve concerns about power consumption. The proposed indicators and the fusion method are implemented as an Android App called UPDetector. Via experiments, we show that our App is both effective and energy-efficient in detecting parking/unparking activities.

Community is a basic social unit taking care of groups of families. Since the day community becomes operational, it has a routine cycle of maintenance. And in turn take services as input from multiple vendors and hence generate improved ambience and fully functional services. And for this it require that maintenance issues are registered timely. But the major concern is recording and registration of the complaints for every community issues. These targeted issues are street lights control, speedy vehicle movements, tracking hijacked or vacant parking space, over grown green belt growth, electricity issues, water management control and growth through IOT. Also it is not easy for residents to register complaints via different portals or apps. This paper intends to provide a smart solution using IOT sensors. These sensors would automatically detect the issues and inform the concerned authorities by using an automated complained recording backend. This backend also would manage the complaint register status for further necessary reviews. Problem statement and its criticality are discussed at the beginning of the paper. The paper also intends to detail how to make the sensor system, low maintenance and self-healing. Then the proposed solutions are discussed case by case. The paper also provide a detailed insight of the iot sensors.

The surge in roadway truck freight has intensified the difficulties faced by truck drivers in locating safe and legal parking spaces. Parking shortage on the highway system not only creates logistical challenges, but also has substantial economic implications. To help drivers make safer, more efficient parking decisions and better utilize existing truck parking facilities, Truck Parking Information Management Systems (TPIMS) have been deployed in several states to provide real-time parking information to truck drivers through various communication channels. This dissertation first presents an anomaly detection method to identify sensor failures by tracking structural changes in time series parking data. In addition, a data dashboard is developed to evaluate and visualize the performance of the TPIMS through multidimensional aggregation of parking flow data. It can be observed from the dashboard that after the implementation of TPIMS, the utilization among parking facilities along I80 is more evenly distributed. Such intuitive tools can help agencies evaluate the system performance and make informed decisions. Despite the availability of real-time utilization information provided by truck parking information and management systems, there remains a significant requirement for truck drivers to know the anticipated availability of parking spaces at their intended arrival time. The second part of this dissertation develops predictive models that aid truck drivers in making informed decisions regarding trip planning, specifically focusing on two scenarios: pre-trip planning and en-route decision-making. Several tree-based ensembled machine learning models and a recurrent neural network based architecture is developed to forecast the utilization of parking sites and the results show that the prediction can be achieved with small errors. Models with and without other input attributes (e.g., weather, truck volume data) are discussed and compared at different prediction horizon, which enables various levels of forecasting. The integration of long-term and short-term prediction models aligns more closely with drivers' travel planning tendencies and this will help build a more efficient planning and decision-making process, thus optimizing the transportation industry's overall performance. Finally, to address the issue that the reliability and accuracy will hinder the future adoption of the system, a novel approach to enhance TPIMS is discussed by utilizing image processing techniques. In particular, the state-of-the-art You Only Look Once (YOLO) v5 algorithm is used to detect the occupancy of truck parking sites. By comparing the image processing results with the sensor reported data, sensor fault can be detected. Accordingly, a real time reporting system is proposed to communicate sensor fault result to the system operators in a timely fashion. The experimental results demonstrate the effectiveness and efficiency of the proposed system, and it also provides insights for the development and improvement of truck parking information and management systems.

This paper focuses on an analysis of parking and public transport information and how it is used in various cities. The focus lies on the feasibility of an implementation of such an information system in Vienna. As a first step, this paper concerns the analysis of open data and which benefits could be generated with these open government data. To extend these findings, the next topic is to analyze what is already used in especially Vienna and what could possibly be developed. There is a lot of existing information but most of it is not used in an appropriate way to fulfill the needs of such parking information systems. It has to be investigated which information sources are offered and if they are sufficient for such an implementation. The focus for these achievements will be on data sources which are offered free and accessible via the web. To complete the findings it will be analyzed which applications are on the market and in which areas, more precisely in which cities are such implementations are already successfully used. Another important point is how the raw information is processed and further how it is delivered to the user and their devices. The last part of this paper will focus on commercial complete systems provided by companies like Siemens or Kapsch. Furthermore, systems which are provided by garage operators will be analyzed and examined. Keywords Public Management. Parking Information, Public Transport Information

In today's world, time is the most valuable asset and this paper focuses on developing a smart parking system for smart cities. Its main objective is to assist users to find a vacant location of car parking in a shorter time. The system displays occupied and vacant parking spots on LCD, making it convenient for the users to park. This parking system is based on sensors and cloud server integration. The working principle is divided into two phases. In the first phase, the ultrasonic sensor is used to detect vacant or occupied car parking space and display it on the screen using cloud technology. In the second phase, the improper parking of the car is detected by IR sensors. It alerts the driver with a buzzer if the car is not parked in the parking boundary. Moving further, some important features of this parking system are also discussed followed by, components required and their description. Towards the end of the paper, this parking system is compared with other alternative smart parking systems, the comparisons prove the correctness of our system over others.

This paper proposes a new method based on multiple attribute decision making to search for the optimal parking space in a parking lot. The advantages and limits of different kinds of property weights are investigated in this paper. By improving the limits of subjective weight and combining subjective weight with objective weight, we get an integrated weight. Using this hybrid weight, we introduce how to apply multiple attribute decision making to search for the optimal parking space. Experiments show that this method has a satisfying performance and can find the real-time optimal parking space. The proposed method can be applied to city parking guidance and information system.

The experiments were carried out to study relationship between flower formation and elongation of internode in cereals. In the present report, especially, the effects of temperature on flower formation and elongation of internode in paddy rice plants are concerned. In the experiments, rice varieties NORIN No.11, RIKU No.132 and ZUIHO were used as materials. Seeds of each variety were sowed several times in a thermo-controlled green house (18-25°C) and in the open air (15-30°C), respectively. The time of flower formation was indicated by the double-ridge stage of main shoot apex. But, we have no suitable index for elongation of internodes. So the formation of vacant space (inside the tissue of the node) was taken for a sign of elongation of internodes. Dissected stem tips were fixed in F.A.A., and were embedded in paraffin wax. Serial sections cut (at about 15 μ) of them were stained with 1% gentian violet solution. In an early stage of vegetative growth, we observed none of the nodes in the shoot apex. However, small cell bands were observed below the growing point of the apex nearly at the middle stage of vegetative growth. The authors termed it node formation (Plate B). Moreover, the small cell bands splitted then and there appeared so-called vacant spaces among those bands. (Plate C). Finally, visible elongation of internodes was observed. The elongation of internodes in highly thermo-sensitive varieties began at an earlier stage than that in low thermo-sensitive varieties. Namcly, the elongation of internodes in NORIN No.11 occurred at 6-leaves stage, while that in RIKU No.132 and in ZUIHO took place at 10-leaves stage. We observed no alteration in the time of these stages by applying high or low temperatures. So, it could be concluded that temperature has no influence on the node formation in paddy rice, though the growth of plant was remarkably delayed at lower temperatures. On the contrary, the flower formation in high thermo-sensitive varieties (NORIN No.11) were delayed by low temperatures. But, the flower formation in low thermo-sensitive varieties is rather photo-controlled than thermo-controlled. Thus we may conclude that there is no close relations between flower formation and beginning of elongation of internodes in paddy rice plants, though visible elongation of internodes is stimulated by flower formation